{"id":"W4407129083","doi":"10.1109/tcomm.2025.3538825","title":"Joint Transmission Mode Selection and Scheduling for AoI Minimization in NOMA-Capable WP-IoT Networks: A Deep Transfer Learning Solution","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noma; Computer science; Joint (building); Scheduling (production processes); Transmission (telecommunications); Selection (genetic algorithm); Internet of Things; Minification; Transfer of learning; Data transmission; Distributed computing; Electronic engineering; Computer network; Artificial intelligence; Telecommunications link; Mathematical optimization; Engineering; Telecommunications; Embedded system; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000240883,0.0002269695,0.0002606715,0.0004564779,0.0006822161,0.00006514149,0.0002172885,0.0002522503,0.00001196418],"category_scores_gemma":[0.00000694909,0.0002784748,0.000103704,0.0007937881,0.00006050763,0.000228635,0.000002412313,0.0007075808,0.000001821255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002458649,"about_ca_system_score_gemma":0.00004246711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007547385,"about_ca_topic_score_gemma":0.001357591,"domain_scores_codex":[0.9986902,0.0001381339,0.0004848162,0.0002635572,0.00009499116,0.0003282391],"domain_scores_gemma":[0.9990981,0.0003002955,0.0000288066,0.0004181632,0.0000848713,0.00006973283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004445071,0.0001156941,0.00002129559,0.00007274886,0.00004920975,1.04734e-7,0.0004506851,0.9483258,0.004359323,0.000279184,0.00002085814,0.04626063],"study_design_scores_gemma":[0.0009110723,0.00004640393,0.0001427745,0.0003097366,0.00008142232,0.000002712001,0.0001436309,0.993503,0.003574816,0.0002045864,0.0008501882,0.0002296904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01771585,0.001015357,0.9790537,0.000563439,0.0001706038,0.0006871156,0.000005696247,0.0004174857,0.0003707618],"genre_scores_gemma":[0.9693096,0.002401197,0.02750113,0.00003832151,0.00002359066,0.0005358388,0.00003954016,0.00005206377,0.00009873284],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9515938,"threshold_uncertainty_score":0.9999667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170979122230098,"score_gpt":0.2476384517630057,"score_spread":0.2305405395399959,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}